UNLABELLED: The development of a successful PET or SPECT molecular imaging probe is a complex, time-consuming, and expensive process that suffers from high attrition. To address this problem, we have developed a biomathematical modeling approach that aims to predict the in vivo performance of radioligands directly from in silico/in vitro data. METHODS: The method estimates the in vivo nondisplaceable and total uptake of a ligand in a target tissue using a standard input function and a 1-tissue-compartment model with a parsimonious parameter set (influx rate constant K(1), efflux rate constant k(2), and binding potential BP(ND)) whose values are predicted from in silico/in vitro data including lipophilicity, molecular volume, free fraction in plasma and tissue, target density, affinity, perfusion, capillary surface area, and apparent aqueous volume in plasma and tissue. The coefficient of variation of the BP(ND) (%COV[BP(ND)]) metric, derived from Monte Carlo simulations, is used to estimate the in vivo performance of candidate compounds. A total of 28 compounds for 10 targets was evaluated using our method to predict their in vivo performance and validated against measured in vivo PET data in the Yorkshire/Danish Landrace pig. RESULTS: The predicted K(1), k(2), and BP(ND) values were generally consistent with the values estimated from in vivo PET data. The model resulted in small %COV[BP(ND)] values for widely accepted good ligands such as (11)C-flumazenil (2.02%) and (11)C-raclopride (2.55%), whereas higher values resulted from poor ligands such as (11)C-(R)-PK11195 (13.34%). Of 4 candidates for the GlyT1 transporter, the model selected (11)C-GSK931145 (2.11%) as the most promising ligand, which was consistent with historical decisions made on the in vivo PET data. CONCLUSION: A biomathematical modeling approach has the potential to predict the in vivo performance of ligands from in silico/in vitro data and aid in the development of molecular imaging probes.
UNLABELLED: The development of a successful PET or SPECT molecular imaging probe is a complex, time-consuming, and expensive process that suffers from high attrition. To address this problem, we have developed a biomathematical modeling approach that aims to predict the in vivo performance of radioligands directly from in silico/in vitro data. METHODS: The method estimates the in vivo nondisplaceable and total uptake of a ligand in a target tissue using a standard input function and a 1-tissue-compartment model with a parsimonious parameter set (influx rate constant K(1), efflux rate constant k(2), and binding potential BP(ND)) whose values are predicted from in silico/in vitro data including lipophilicity, molecular volume, free fraction in plasma and tissue, target density, affinity, perfusion, capillary surface area, and apparent aqueous volume in plasma and tissue. The coefficient of variation of the BP(ND) (%COV[BP(ND)]) metric, derived from Monte Carlo simulations, is used to estimate the in vivo performance of candidate compounds. A total of 28 compounds for 10 targets was evaluated using our method to predict their in vivo performance and validated against measured in vivo PET data in the Yorkshire/Danish Landrace pig. RESULTS: The predicted K(1), k(2), and BP(ND) values were generally consistent with the values estimated from in vivo PET data. The model resulted in small %COV[BP(ND)] values for widely accepted good ligands such as (11)C-flumazenil (2.02%) and (11)C-raclopride (2.55%), whereas higher values resulted from poor ligands such as (11)C-(R)-PK11195 (13.34%). Of 4 candidates for the GlyT1 transporter, the model selected (11)C-GSK931145 (2.11%) as the most promising ligand, which was consistent with historical decisions made on the in vivo PET data. CONCLUSION: A biomathematical modeling approach has the potential to predict the in vivo performance of ligands from in silico/in vitro data and aid in the development of molecular imaging probes.
Authors: Roger N Gunn; Scott G Summerfield; Cristian A Salinas; Kevin D Read; Qi Guo; Graham E Searle; Christine A Parker; Phil Jeffrey; Marc Laruelle Journal: J Cereb Blood Flow Metab Date: 2012-01-25 Impact factor: 6.200
Authors: Anders Ettrup; Martin Hansen; Martin A Santini; James Paine; Nic Gillings; Mikael Palner; Szabolcs Lehel; Matthias M Herth; Jacob Madsen; Jesper Kristensen; Mikael Begtrup; Gitte M Knudsen Journal: Eur J Nucl Med Mol Imaging Date: 2010-12-21 Impact factor: 9.236
Authors: Pablo M Rusjan; Alan A Wilson; Laura Miler; Ian Fan; Romina Mizrahi; Sylvain Houle; Neil Vasdev; Jeffrey H Meyer Journal: J Cereb Blood Flow Metab Date: 2014-02-12 Impact factor: 6.200
Authors: Qi Guo; David R Owen; Eugenii A Rabiner; Federico E Turkheimer; Roger N Gunn Journal: J Cereb Blood Flow Metab Date: 2014-04-16 Impact factor: 6.200